Fuel consumption predictions using associative memories

ABSTRACT

A method, system, and computer program product for predicting fuel consumption for a vehicle operation is provided. An associative memory device is accessed, using one or more attributes of an operation plan for the planned vehicle operation, to determine at least one historical vehicle operation from a plurality of historical vehicle operations that is similar to the planned vehicle operation. The associative memory device contains data for a plurality of attributes and collected from the plurality of historical vehicle operations. The at least one historical vehicle operation is determined by applying a respective weight defined within the associative memory device to data values for each of the plurality of attributes. Fuel consumption data for the planned vehicle operation is predicted, based on historical fuel consumption data corresponding to the at least one historical vehicle operation. The predicted fuel consumption data is output.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/061,699 filed Mar. 4, 2016. The above-mentioned applications arehereby incorporated by reference.

BACKGROUND

Calculating a fuel load for a vehicle operation can be a challengingundertaking. Various methods and assumptions can be used to estimate anamount of fuel needed to complete a vehicle operation, such as a flightof a commercial aircraft from a departure airport to an arrival airport.Properly calculating the fuel load for a vehicle operation is importantto ensure success of the operation. For example, if too little fuel ison board the vehicle, then the vehicle may have to stop prematurely toget more fuel. For example, a commercial aircraft may have to make anunscheduled stop for additional fuel if the calculated fuel load isinsufficient. As another example, if too much fuel as a vehicle, thenthe vehicle needlessly carries extra weight, which reduces theefficiency and increases the cost of the vehicle operation.

SUMMARY

According to one aspect, a computer-implemented method for calculating apredicted fuel consumption for a planned vehicle operation is provided.The method includes accessing an associative memory device, using one ormore attributes of an operation plan for the planned vehicle operation,to determine at least one historical vehicle operation from a pluralityof historical vehicle operations that is similar to the planned vehicleoperation, where the associative memory device contains data for aplurality of attributes and collected from the plurality of historicalvehicle operations, and where the at least one historical vehicleoperation is determined by applying a respective weight defined withinthe associative memory device to data values for each of the pluralityof attributes. The method also includes predicting fuel consumption datafor the planned vehicle operation, based on historical fuel consumptiondata corresponding to the at least one historical vehicle operation.Additionally, the method includes outputting the predicted fuelconsumption data.

According to one aspect, a system includes one or more computerprocessors, an associative memory device and a computer memory. Thecomputer contains computer program code that, when executed by operationof the one or more computer processors, performs an operation forcalculating a predicted fuel consumption for a planned vehicleoperation. The operation includes accessing the associative memorydevice, using one or more attributes of an operation plan for theplanned vehicle operation, to determine at least one historical vehicleoperation from a plurality of historical vehicle operations that issimilar to the planned vehicle operation, where the associative memorydevice contains data for a plurality of attributes and collected fromthe plurality of historical vehicle operations, and where the at leastone historical vehicle operation is determined by applying a respectiveweight defined within the associative memory device to data values foreach of the plurality of attributes. Additionally, the operationincludes predicting fuel consumption data for the planned vehicleoperation, based on historical fuel consumption data corresponding tothe at least one historical vehicle operation. The operation alsoincludes outputting the predicted fuel consumption data.

According to one aspect, a computer-implemented method for determining apredicted fuel consumption for a planned vehicle operation is provided.The method includes generating a query configured to return predictedfuel consumption data for an operation plan for the planned vehicleoperation. The method also includes transmitting the generated query toa remote system. The remote system is configured to access anassociative memory device, using one or more attributes of the operationplan for the planned vehicle operation, to determine at least onehistorical vehicle operation from a plurality of historical vehicleoperations that is similar to the planned vehicle operation, where theassociative memory device contains data for a plurality of attributesand collected from the plurality of historical vehicle operations, andwhere the at least one historical vehicle operation is determined byapplying a respective weight defined within the associative memorydevice to data values for each of the plurality of attributes. Theremote system is further configured to predict fuel consumption data forthe planned vehicle operation, based on historical fuel consumption datacorresponding to the at least one historical vehicle operation. Themethod includes receiving, from the remote system, the predicted fuelconsumption data.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a system according to one aspect forimplementing an associative memory to predict fuel consumption;

FIG. 2 is a block diagram illustrating an exemplary instantiation of ahistorical vehicle operation stored in an associative memory;

FIG. 3 is an illustration of a graphical user interface according to oneaspect for entering weightings for attributes of historical vehicleoperations and allowable radii for values of certain attributes;

FIG. 4A is a graphical representation of exemplary values of anattribute that are highly correlated with fuel consumption;

FIG. 4B is a graphical representation of exemplary values of anotherattribute that are highly correlated with fuel consumption;

FIG. 4C is a graphical representation of exemplary values of anotherattribute that are weakly correlated with fuel consumption;

FIG. 4D is a graphical representation of exemplary values of anotherattribute that are not correlated with fuel consumption;

FIG. 4E is a graphical representation of exemplary values of anotherattribute that are correlated by certain groupings with fuelconsumption;

FIG. 5 is a block diagram of illustrating exemplary returned vehicleoperations from an associative memory and extracted fuel consumptiondata from the returned operations;

FIG. 6 is a flow chart for a method according to one aspect forpredicting fuel consumption for a vehicle operation using an associativememory of historical vehicle operations; and

FIG. 7 is a flow chart for a method according to one aspect forautomatically determining weightings of attributes for historicalvehicle operations in an associative memory.

DETAILED DESCRIPTION

In the following, reference is made to aspects presented in thisdisclosure. However, the scope of the present disclosure is not limitedto specific described aspects. Instead, any combination of the followingfeatures and elements, whether related to different aspects or not, iscontemplated to implement and practice contemplated aspects.Furthermore, although aspects disclosed herein may achieve advantagesover other possible solutions or over the prior art, whether or not aparticular advantage is achieved by a given aspect is not limiting ofthe scope of the present disclosure. Thus, the following aspects,features, and advantages are merely illustrative and are not consideredelements or limitations of the appended claims except where explicitlyrecited in a claim(s). Likewise, reference to “the invention” or “thedisclosure” shall not be construed as a generalization of any inventivesubject matter disclosed herein and shall not be considered to be anelement or limitation of the appended claims except where explicitlyrecited in a claim(s).

In various aspects described herein, fuel consumption data from one ormore historical vehicle operations is used to predict fuel consumptionfor a planned vehicle operation. Instantiations of historical vehicleoperations are stored in an associative memory, and attributes of thehistorical vehicle operations are weighted based on their significancewith respect to fuel consumption. Values of attributes for the plannedvehicle operation are used to search the associative memory and theassociative memory returns historical vehicle operations that are mostsimilar to the planned vehicle operation based on the values of theattributes. Fuel consumption data for the returned historical vehicleoperations is extracted and a predicted fuel consumption for the plannedvehicle operation is calculated based on the returned fuel consumptiondata. The predicted fuel consumption can be incorporated into a vehicleoperation plan (e.g., a load sheet for a commercial flight operation)that indicates a fuel load for the planned vehicle operation.

FIG. 1 illustrates a system 100 according to one aspect for predictingfuel consumption for a planned vehicle operation. The system 100includes a computer processor 102 and a memory 104. The memory 104includes an associative memory data structure 106, the fuel predictionapplication 112, and an associative memory building application 114. Theassociative memory 106 includes instantiations of historical vehicleoperations 108, collectively. FIG. 1 illustrates four historical vehicleoperations 108: a first historical vehicle operation 108 a, a secondhistorical vehicle operation 108 b, a third historical vehicle operation108 c, and a fourth historical vehicle operation 108 d. In use, theassociative memory 106 would likely include hundreds, thousands, ormillions of historical vehicle operations 108. As we discussed ingreater detail with respect to FIG. 2, below, the historical vehicleoperations 108 include different attribute categories, and the values ofthe attribute categories define parameters of the historical vehicleoperations 108. For example, referring to commercial aircraftoperations, a historical vehicle operation may include attributecategories that identify the aircraft type, the departure airport, thearrival airport, the cruise altitude, the cruise speed, and the distancetraveled. The associative memory 106 can include attribute weightings110 that indicate relative importance of certain attributes relative toother attributes. For example, continuing the example of the commercialaircraft operation, the departure airport and arrival airport may beless important than the distance traveled. As a result, the weightingsfor the departure airport attribute and the arrival airport attributewould be lower than a weighting for the distance traveled attribute.

The system 100 can also include an input 116 and an output 118. Theinput 116 and the output 118 can communicate with a vehicle planningsystem 120. The vehicle planning system 120 can include a vehicleplanning application 122 and a vehicle operation plan 124. The vehicleplanning application 122 is executable by the vehicle planning system120 to prepare the vehicle operation plan 124 for a planned vehicleoperation. The vehicle operation plan 124 for the planned vehicleoperation includes planned values for the various attribute categories.The planned values for the attribute categories in the vehicle operationplan 124 can be transmitted to the system 100 via the input 116.Similarly, after the system 100 predicts fuel consumption for theplanned vehicle operation, as discussed in greater detail below, thesystem 100 can output the predicted fuel consumption to the vehicleplanning system 120 via the output 118. The vehicle planning application122 can then incorporate the predicted fuel consumption for the plannedvehicle operation into the vehicle operation plan 124.

FIG. 2 is a block diagram of an exemplary historical vehicle operation108 n stored in the associative memory 106. The exemplary historicalvehicle operation 108 n includes identifying information such as aflight number 202 and a date 204 of the flight for a commercialaircraft. The exemplary historical vehicle operation 108 n also includesattribute data 206. The attribute data 206 is arranged as attributecategories, indicated in column 208, and values for the differentcategories, indicated in column 210. The exemplary attribute data 206includes an aircraft type attribute category 212, and a value 214 of737-800 (i.e., a Boeing 737-800). The exemplary attribute data 206includes an aircraft serial number attribute category 216, and a value218 of 12345. The exemplary attribute data 206 includes a departureairport attribute category 220, and a value 222 of SEA, which is theairport code for Seattle-Tacoma International Airport. The exemplaryattribute data 206 includes an arrival airport attribute category 224,and a value 226 of “ORD”, which is the airport code for O'HareInternational Airport in Chicago, Ill. The exemplary attribute data 206includes a number of passengers attribute category 228, and a value 230of 122 (i.e., 122 passengers). The exemplary attribute data 206 alsoincludes a number of crew attribute category 232, and a value 234 of 5(i.e., five passengers). The exemplary attribute data 206 also includesa takeoff weight attribute category 236 and a value 238 of 170,000(i.e., 170,000 pounds). The exemplary attribute data 206 also includes aprecipitation attribute category 240, and a value 242 of “heavy rain.”The attribute data 206 also includes a clouds attribute category 244,and a value 246 of “scattered.” The attribute data 206 also includes afog attribute category 248, and a value 250 of “none.” The attributedata 206 also includes a cruising altitude attribute category 252 and avalue 254 of 34,000 (i.e., 34,000 feet above sea level). The attributedata 206 also includes a cruising speed attribute category 256 and avalue 258 of 0.81 (i.e., Mach 0.81). The attribute data 206 alsoincludes a distance attribute category 260 and a value 262 of 1,721(i.e., 1,721 nautical miles). The attribute categories and values of theattribute data 206 shown in FIG. 2 are exemplary. In various aspects,the attribute data 206 could include many additional attributecategories and/or values.

The attribute data 206 includes attribute categories that have numericor quantifiable values as well as attribute categories that havenon-numeric or non-quantifiable values. For example, the cruisingaltitude attribute category 252, the cruising speed attribute category256 and the distance attribute category 260 all have numeric orquantifiable values. By contrast, the precipitation attribute category240, the clouds attribute category 244, and the fog attribute category248 have non-numeric or non-quantifiable values.

As discussed above, the associative memory 106 can also includeattribute weightings 110 that indicate relative importance of anattribute category relative to other attribute categories. In variousaspects, the attribute weightings 110 could be determined by a user,such as a subject matter expert. For example, an airline dispatcher mayset weightings for attribute categories in the associative memorybuilding application 114. In various other aspects, the attributeweightings 110 could be determined by the associative memory buildingapplication 114 executing on the computer processor 102.

FIG. 3 illustrates an exemplary graphical user interface 300 that asubject matter expert could use to set the attribute weightings 110 forthe attribute categories 208 discussed above with reference to FIG. 2.In the exemplary graphical user interface 300, the attribute categoriescan be set to one of five different weightings 302: a low weighting 304,a medium-low-weighting 306, a medium weighting 308, a medium-highweighting 310, and a high weighting 312. Continuing the example above,the aircraft type attribute category 212 is assigned a high weighting312. The aircraft serial number attribute category 216 is assigned a lowweighting 304. The departure airport attribute category 220 is assigneda medium weighting 308. The arrival airport attribute category 224 isassigned medium weighting 308. The number of passengers attributecategory 228 is assigned a medium weighting 308. The number of crewattribute category 232 is assigned a medium-low weighting 306. Thetakeoff weight attribute category 236 is assigned a medium-highweighting 310. The precipitation attribute category 240 is assigned amedium-low weighting 306. The clouds attribute category 244 is assigneda medium-low weighting 306. The fog attribute category 248 is assignedmedium-low weighting 306. The cruise altitude attribute category 252 isassigned a medium-high weighting 310. The cruising speed attributecategory 256 is assigned medium-high weighting 310. The distanceattribute category 260 is assigned a high weighting 312. In use, a user(e.g., a subject matter expert) could interact with the graphical userinterface 300 to select weightings for the various categories using acomputer mouse, touch screen, keyboard, or other input device. Theweightings influence the results returned from the associative memory(e.g., the associative memory 106 in FIG. 1).

In various aspects, a user could also use the graphical user interface300 to select allowable ranges or radii for values of categories thatare acceptable for returned historical vehicle operations. The graphicaluser interface 300 illustrated in FIG. 3 includes an allowable radiusfield 330, which a subject matter expert or other user could enter suchradii for at least certain categories of attributes. For example, theallowable radius field 330 includes a first radius 332 with a value of0.05 or 5%. Using the first radius 332, a value for the number ofpassengers attribute category 228 for a historical vehicle operationwill only be considered similar to a predicted value for a futurevehicle operation if the value is within the percentage indicated by theradius. For example, suppose that a future vehicle operation ispredicted to have 100 passengers (i.e., a value for the number ofpassengers attribute category 228 of 100). In such instances, historicalvehicle operations that have a value for the number of passengersattribute category 228 of between 95 and 105 would be consideredsimilar.

The allowable radius field 330 includes additional radii for otherattribute categories. For example, the allowable radius field 330includes a second radius 334 for the number of crew attribute category232, which is set to a value of 0.00, meaning the value for the numberof crew attribute category 232 of historical vehicle operations mustexactly match the value of the number of crew attribute category 232 fora planned vehicle operation to be considered similar. As anotherexample, the allowable radius field 330 includes a third radius 336 forthe takeoff weight attribute category 236, which is set to a value of0.10 or 10%, meaning the value for the takeoff weight attribute category236 of historical vehicle operations must be within 10% of the value ofthe takeoff weight attribute category 236 for a planned vehicleoperation to be considered similar. As another example, the allowableradius field 330 includes a fourth radius 338 for the cruising altitudeattribute category 252, which is set to a value of 0.10 or 10%, meaningthe value for the cruising altitude attribute category 252 of historicalvehicle operations must be within 10% of the value of the cruisingaltitude attribute category 252 for a planned vehicle operation to beconsidered similar. As another example, the allowable radius field 330includes a fifth radius 340 for the cruising speed attribute category256, which is set to a value of 0.10 or 10%, meaning the value for thecruising speed attribute category 256 of historical vehicle operationsmust be within 10% of the value of the cruising speed attribute category256 for a planned vehicle operation to be considered similar. As anotherexample, the allowable radius field 330 includes a sixth radius 342 forthe distance attribute category 260, which is set to a value of 0.15 or15%, meaning the value for the distance attribute category 260 ofhistorical vehicle operations must be within 15% of the value of thedistance attribute category 260 for a planned vehicle operation to beconsidered similar.

The user provided weightings and/or allowable radii can be used by thefuel prediction application 112 executing on the computer processor 102to influence which historical vehicle operations 108 in the associativememory 106 are identified as being similar to a planned vehicleoperation (e.g., the vehicle operation plan 124 stored in the vehicleplanning system 120).

In various aspects, the associative memory building application 114 canautomatically determining weightings for various attribute categories.For example, in one aspect, the associative memory building application114 can assign high weightings to attribute categories having valuesthat are strongly correlated to fuel consumption across historicalvehicle operations and can assign low weightings to attribute categorieshaving values that are weakly correlated or not correlated to fuelconsumption across historical vehicle operations. For example, FIG. 4Aillustrates a chart 400 of values for a particular attribute categoryagainst fuel consumption for the various historical vehicle operations.Each instance of a value for the attribute category and an associatedfuel consumption is represented as an open square 402. As depicted inFIG. 4A, a line 404 is fit through the instances 402, and the instances402 are relatively compact with respect to the line 404. Additionally,fuel consumption increases as the value of the attribute increases.Thus, the values for the particular attribute category are highly,positively correlated with fuel consumption. For example, in the contextof commercial aviation, fuel consumption could be highly, positivelycorrelated with values of an attribute category such as takeoff weightand/or number of passengers.

FIG. 4B illustrates a chart 410 of values for a particular attributecategory against fuel consumption for the various historical vehicleoperations in which the values are negatively correlated with fuelconsumption. Each instance of a value for the attribute category and anassociated fuel consumption is represented as an open square 412. Asdepicted in FIG. 4B, a line 414 fits through the instances 412, and theinstances 412 are relatively compact with respect to the line 414.Additionally, fuel consumption increases as the value of the attributedecreases. Thus, the values for the particular attribute category arehighly, negatively correlated with fuel consumption. For example, in thecontext of commercial aviation, fuel consumption could be highly,negatively correlated with values of an attribute category such asaircraft serial number. In many instances, aircraft serial numbersincrease in a sequential manner, meaning that older airframes have lowerserial numbers the newer airframes. Over time, an airframe generallygets heavier as equipment is added and/or as insulation absorbs water,for example. Thus, older aircraft with lower serial numbers may weighmore and use more fuel on a given flight than a newer aircraft. As aresult, values for aircraft serial number attribute category could benegatively correlated with fuel consumption.

FIG. 4C illustrates a chart 420 of values for a particular attributecategory against fuel consumption for the various historical vehicleoperations in which the values are weakly correlated with fuelconsumption. Each instance of a value for an attribute category and anassociated fuel consumption is represented as the square 422. Asdepicted in FIG. 4C, a line 424 fits through the instances 422, but theinstances 422 are relatively spread out with respect to the line 424.

FIG. 4D illustrates a chart 430 in which values for a particularattribute category for the various historical vehicle operations are notcorrelated with fuel consumption. Each instance of a value for anattribute category and an associated fuel consumption is represented asa square 432, and there is no line that adequately fits to the data.

FIG. 4E illustrates a chart 440 of values for a particular attributecategory against fuel consumption for the various historical vehicleoperations in which the values are correlated with fuel consumption in astructured manner. For example, the exemplary data illustrated in thechart 440 includes a first set of data 442 (i.e., instances ofhistorical vehicle operations) having a first value for the attributecategory, a second set of data 444 (i.e., instances of historicalvehicle operations) having a second value for the attribute category,and a third set of data 446 (i.e., instances of historical vehicleoperations) having a third value for the attribute category. Each of thesets of data 442, 444, and 446 covers a range of fuel consumptionvalues, and a line 448 can fit through the data such that there is atleast a weak correlation. Such sets of data could occur with anattribute category such as a model designation for aircraft. Forexample, the Boeing 737 includes several sub models such as the 737-700,the 737-800, and the 737-900, wherein the -800 model is larger than the-700 model and the -900 model is larger than the -800 model. Referringagain to the chart 440 in FIG. 4E, the first data set 442 could beinstances of historical vehicle operations on 737-700 models, the seconddata set 444 could be instances of historical vehicle operations on737-800 models, and the third data set 446 could be instances ofhistorical vehicle operations on 737-900 models.

As discussed above, aspects of the associative memory buildingapplication 114 executing on the computer processor 102 analyze thevalues of the attribute categories across the historical vehicleoperations 108 stored in the associative memory 106 to identify anycorrelations between a particular value and fuel consumption. Attributecategories that are highly correlated with fuel consumption could begiven a high weighting, attribute categories that are weakly correlatedwith fuel consumption could be given a medium weighting, and attributecategories that are not correlated with fuel consumption could be givena low weighting, for example. The resulting attribute weightings 110 canbe stored in the associative memory 106. Referring again to FIG. 3, theassociative memory building application 114 could output the determinedweightings to a graphical user interface, such as the graphical userinterface 300, to illustrate the weightings that have been assigned tothe different attribute categories. In at least one aspect, a subjectmatter expert or other user could use the graphical user interfaceoverride or change the weightings determined by the associative memorybuilding application 114.

After the weightings and/or any allowable radii have been determined forthe various attribute categories, the associative memory 106 ofhistorical vehicle operations 108 can be searched for operations thatare similar to a vehicle operation plan 124 for a future vehicleoperation. In at least one aspect, a similarity score for a particularhistorical vehicle operation can be determined mathematically based onthe weightings for the different attribute categories and differences ofthe values for the attribute categories between the particularhistorical vehicle operation and the planned vehicle operation. Forexample, the similarity score could be calculated according to Equation(1):

$\begin{matrix}{{{{SIMILARITY}\mspace{14mu}{SCORE}} = \frac{1}{{W_{a}\Delta\; V_{a}} + {W_{b}\Delta\; V_{b}} + \ldots + {W_{n}\Delta\; V_{n}}}};} & (1)\end{matrix}$where W_(a) is a weighting for a first attribute category, W_(b) is aweighting for a second attribute category, W_(n) is a weighting for annth attribute category, ΔV_(a) is a difference in the value for thefirst attribute category between the particular historical vehicleoperation and the planned vehicle operation, ΔV_(b) is a difference inthe value for the second attribute category between the particularhistorical vehicle operation and the planned vehicle operation, andΔV_(n) is a difference in the value for the nth attribute categorybetween the particular historical vehicle operation and the plannedvehicle operation. In various aspects, the values could be normalizedsuch that the units or scale of values for one attribute category haveapproximately equal significance to the units or scale of values forother attribute categories. For example, the values could be normalizedsuch that a takeoff weight difference between a historical vehicleoperation and the planned vehicle operation of 10,000 pounds does notcompletely outweigh a cruise speed difference of 10 knots. In variousaspects, such normalization could be provided by the weightings. Invarious aspects, the difference between values can be provided as anabsolute value. The resulting similarity score from Equation (1) will belarger for historical vehicle operations that are very similar to theplanned vehicle operation and smaller for historical vehicle operationsthat are less similar or very different from to the planned vehicleoperation.

In various aspects, different equations could be used to calculate asimilarity score. Depending on the equation, the relationship betweenthe similarity of a particular historical vehicle operation in theplanned vehicle operation and the score could be different. For example,a similarity score could be calculated based on equation that is theinverse of Equation (1). In such a case, the resulting similarity scorewill be larger for historical vehicle operations that are very differentfrom the planned vehicle operation and smaller for historical vehicleoperations that are most similar to the planned vehicle operation.

FIG. 5 illustrates a table 500 of exemplary historical vehicleoperations that have been returned after the fuel prediction application112 mathematically determined which historical vehicle operations 108are most similar to a planned vehicle operation. The table 500 includesa first column 502 to indicate scores (i.e., similarity scores computedaccording to Equation (1) or another equation). The table 500 optionallyincludes a second column 504 that includes identification informationfor the different historical vehicle operations, such as a flight numberof commercial flight and/or a date of the flight. The table 500 alsoincludes a fuel consumption column 506 that identifies the amount offuel consumed by the determined historical vehicle operations.

The first returned historical vehicle operation 510 has a score of 1.00and a fuel consumption of 20,500 pounds. The second returned historicalvehicle operation 512 has a score of 0.98 and a fuel consumption of21,000 pounds. The third returned historical vehicle operation 514 has ascore of 0.95 and a fuel consumption of 19,500 pounds. The fourthreturned historical vehicle operation 516 has a score of 0.95 and a fuelconsumption of 21,250 pounds. The fifth historical vehicle operation 518has a score of 0.94 and a fuel consumption of 23,000 pounds. The sixthhistorical vehicle operation 520 has a score of 0.91 and a fuelconsumption of 19,700 pounds. The seventh historical vehicle operation522 has a score of 0.90 and a fuel consumption of 19,800 pounds.

The scores can be normalized with respect to each other. For example,with reference to Equation (1) discussed above, the resulting similarityscores may have a wide range of values. In at least one aspect, thescores for returned historical vehicle operations could be normalized bydividing each of the scores by the highest score such that the highestscore is equal to 1.00 and the other scores will have values equal to orless than 1.00, for example. In other aspects, the scores could benormalized in other manners.

The number of historical vehicle operations returned by the fuelprediction application 112 can be determined in several different ways.For example, the fuel prediction application 112 could return allhistorical vehicle operations with a score above or below a thresholdvalue. As another example, the fuel prediction application 112 couldreturn the ten historical vehicle operations (or another number ofhistorical vehicle operations) with the highest or lowest scores.

The fuel prediction application 112 can predict fuel consumption for theplanned vehicle operation based on the returned historical vehicleoperations in table 500. In at least one aspect, the fuel predictionapplication 112 could select the highest fuel consumption from the table500 and use the selected highest fuel consumption as the predicted fuelconsumption for the planned vehicle operation. For example, the fuelprediction application 112 could select the 23,000 pounds of fuelconsumption from the fifth historical vehicle operation 518 and outputthe 23,000 pounds of fuel consumption to the vehicle planning system120. The vehicle planning application 122 in the vehicle planning system120 then inserts the 23,000 pounds as the predicted fuel consumption inthe vehicle operation plan 124 for the planned vehicle operation.

In another aspect, the fuel prediction application 112 could calculate amean fuel consumption from the returned historical vehicle operations.For example, the fuel consumption values for the seven historicalvehicle operations 510, 512, 514, 516, 518, 520, and 522 in table 500results in an average fuel consumption value of 20,678 pounds of fuel.The fuel prediction application 112 could select the 20,678 pounds offuel consumption and output the 20,678 pounds of fuel consumption to thevehicle planning system 120. The vehicle planning application 122 theninserts the 20,678 pounds of fuel consumption as the predict fuelconsumption in the vehicle operation plan 124 for the planned vehicleoperation.

In another aspect, the fuel prediction application 112 calculates aweighted average fuel consumption from the returned historical vehicleoperations, wherein the weightings are based on the similarity scores.For example, the weighted average fuel consumption could be calculatedaccording to equation 2:

${{{Predicted}{\mspace{11mu}\;}{Fuel}\mspace{14mu}{Consumption}} = \frac{{w_{a}f_{a}} + {w_{b}f_{b}} + \ldots + {w_{n}f_{n}}}{\left( {w_{a} + w_{b} + \ldots + w_{n}} \right)}};$wherein w_(a) is the score of the first returned historical vehicleoperation, w_(b) is the score of the second returned historical vehicleoperation, w_(n) is the score of the nth returned historical vehicleoperation, f_(a) is the fuel consumption of the first returnedhistorical vehicle operation, f_(b) is the fuel consumption of thesecond returned historical vehicle operation, and f_(n) is the fuelconsumption of the nth returned historical vehicle operation. Forexample, the fuel consumption values for the seven historical vehicleoperations 510, 512, 514, 516, 518, 520, and 522 in table 500 results ina weighted average fuel consumption value according to Equation (2) of20,683 pounds of fuel. The fuel prediction application 112 could selectthe 20,683 pounds of fuel consumption and output the 20,683 pounds offuel consumption to the vehicle planning system 120. The vehicleplanning application 122 then inserts the 20,683 pounds of fuelconsumption as the predict fuel consumption in the vehicle operationplan 124 for the planned vehicle operation.

In various aspects, the fuel prediction application 112 could use othersuitable equations or selection criteria to identify fuel consumptionfor the planned vehicle operation from the fuel consumption datareturned from the historical vehicle operations. For example, the fuelprediction application 112 could use a median fuel consumption valuefrom the returned historical vehicle operations or a minimum fuelconsumption value from the returned historical vehicle operations.

FIG. 6 is a flow chart for a method 600 for predicting fuel consumptionfor a planned vehicle operation. In block 602 of the method 600, anoperation plan for the planned vehicle operation is received. Forexample, with reference to FIG. 1, the system 100 could receive thevehicle operation plan 124 from the vehicle planning system 120 via theinput 116. The operation plan includes values for various attributecategories for the planned vehicle operation. For example, in thecontext of commercial aircraft operation, the various attributecategories can include a departure airport, an arrival airport, adistance traveled, a cruise altitude, cruise speed, a planned number ofpassengers, and many other attributes. In block 604, the values for theattribute categories can be extracted from the operation plan. Forexample, the fuel prediction application 112 could extract the valuesfor the attribute categories from the operation plan. In block 606, arequest is issued to an associative memory for at least one historicalvehicle operation and, in block 608, at least one historical vehicleoperation can be returned from an associative memory of historicalvehicle operations. Again referring to FIG. 1, the fuel predictionapplication 112 could return from the associative memory 106 historicalvehicle operations 108 that are mathematically calculated to be similarto planned vehicle operation based on a comparison of the extractedvalues of attribute categories from the vehicle operation plan 124 andvalues of the attribute categories for the historical vehicle operations108. In block 610, historical fuel consumption data from the returnedhistorical vehicle operations is extracted. For example, the fuelprediction application 112 could extract the fuel consumption data fromthe returned historical vehicle operations 108. In block 612, fuelconsumption for the planned vehicle operation based on the extractedhistorical fuel consumption data can be predicted. For example, the fuelprediction application 112 could determine a maximum fuel consumptionvalue, an average fuel consumption value, a median fuel consumptionvalue, a weighted average fuel consumption value, or some other fuelconsumption value from the fuel consumption data for the returnedhistorical vehicle operations and identify the determined value as apredicted fuel consumption for the planned vehicle operation. In block614, the predicted fuel consumption data is output. For example, thefuel prediction application 112 output the predicted fuel consumptiondata to the vehicle planning system 120 via the output 118 of the system100. The vehicle planning application 122 of the vehicle planning system120 can receive the predicted fuel consumption data and automaticallyincorporate the predicted fuel consumption data into the vehicleoperation plan 124. In various aspects, an amount of fuel equal to theamount predicted by the predicted fuel consumption data could be loadedonto the vehicle. In various aspects, an amount of fuel equal to theamount predicted by the predicted fuel consumption data plus a bufferamount (e.g., 5% of the amount predicted by the predicted fuelconsumption data) could be loaded onto the vehicle.

FIG. 7 is a flowchart for a method 700 for generating or building anassociative memory of historical vehicle operations. In block 702,instantiations of historical vehicle operations are received. Forexample, the system 100 can receive instantiations of historical vehicleoperations 108 via the input 116, and the received instantiations can bestored in the memory 104 of the system 100. In block 704 correlationvalues for the different attributes of the instantiations of historicalvehicle operations with fuel consumption for the instantiations can begenerated. For example, the associative memory building application 114could generate such correlation values as discussed above with referenceto FIGS. 4A-4E. In block 706, weightings of attribute categories can bedetermined based on the correlation values determined in block 704. Forexample, the associative memory building application 114 could assignweightings proportional to the correlation values, and store theattribute weightings 110 in the memory 104. The historical vehicleoperations 108 and the attribute weightings 110 form the associativememory 106.

As discussed above, in various aspects, in block 704 and 706 of themethod 700 are replaced or at least partially overruled by a subjectmatter expert or other user selecting weightings for the variousattribute categories. For example, the subject matter expert could usethe graphical user interface 300 in FIG. 3 to select weightings forvarious attribute categories. In aspects where the associative memorybuilding application 114 determined correlation values and determinedweightings based on the correlation use (block 704 and 706), the subjectmatter expert could overrule such automatically determined weightingsusing the graphical user interface 300.

As planned vehicle operations are performed, the planned values for theattribute categories in the vehicle operation plan can be replaced withactual values to form a historical vehicle operation 108 for inclusionin the associative memory 106. Periodically, the associative memorybuilding application 114 can re-execute blocks 702 through 706 of themethod 700 to incorporate the new historical vehicle operations 108,generate updated correlations, and determine updated weightings based onthe updated correlations.

Data collection in vehicles, such as aircraft, is becoming more and moreprolific over time. As more data is collected and as more instantiationsof flights are added to an associative memory, the fuel usagepredictions will improve as well.

The descriptions of the various aspects have been presented for purposesof illustration, but are not intended to be exhaustive or limited to theaspects disclosed. Many modifications and variations will be apparent tothose of ordinary skill in the art without departing from the scope andspirit of the described aspects. The terminology used herein was chosento best explain the principles of the aspects, the practical applicationor technical improvement over technologies found in the marketplace, orto enable others of ordinary skill in the art to understand the aspectsdisclosed herein.

Aspects described herein may take the form of an entirely hardwareaspect, an entirely software aspect (including firmware, residentsoftware, micro-code, etc.) or an aspect combining software and hardwareaspects that may all generally be referred to herein as a “circuit,”“module” or “system.”

The aspects described herein may be a system, a method, and/or acomputer program product. The computer program product may include acomputer readable storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry outaspects described herein.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations maybe assembler instructions, instruction-set-architecture (ISA)instructions, machine instructions, machine dependent instructions,microcode, firmware instructions, state-setting data, or either sourcecode or object code written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Smalltalk, C++ or the like, and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The computer readable program instructions mayexecute entirely on the user's computer, partly on the user's computer,as a stand-alone software package, partly on the user's computer andpartly on a remote computer or entirely on the remote computer orserver. In the latter scenario, the remote computer may be connected tothe user's computer through any type of network, including a local areanetwork (LAN) or a wide area network (WAN), or the connection may bemade to an external computer (for example, through the Internet using anInternet Service Provider). In some aspects, electronic circuitryincluding, for example, programmable logic circuitry, field-programmablegate arrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects described herein.

Aspects are described herein with reference to flowchart illustrationsand/or block diagrams of methods, apparatus (systems), and computerprogram products according to aspects. It will be understood that eachblock of the flowchart illustrations and/or block diagrams, andcombinations of blocks in the flowchart illustrations and/or blockdiagrams, can be implemented by computer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousaspects. In this regard, each block in the flowchart or block diagramsmay represent a module, segment, or portion of instructions, whichcomprises one or more executable instructions for implementing thespecified logical function(s). In some alternative implementations, thefunctions noted in the block may occur out of the order noted in thefigures. For example, two blocks shown in succession may, in fact, beexecuted substantially concurrently, or the blocks may sometimes beexecuted in the reverse order, depending upon the functionalityinvolved. It will also be noted that each block of the block diagramsand/or flowchart illustration, and combinations of blocks in the blockdiagrams and/or flowchart illustration, can be implemented by specialpurpose hardware-based systems that perform the specified functions oracts or carry out combinations of special purpose hardware and computerinstructions.

Aspects described herein may be provided to end users through a cloudcomputing infrastructure. Cloud computing generally refers to theprovision of scalable computing resources as a service over a network.More formally, cloud computing may be defined as a computing capabilitythat provides an abstraction between the computing resource and itsunderlying technical architecture (e.g., servers, storage, networks),enabling convenient, on-demand network access to a shared pool ofconfigurable computing resources that can be rapidly provisioned andreleased with minimal management effort or service provider interaction.Thus, cloud computing allows a user to access virtual computingresources (e.g., storage, data, applications, and even completevirtualized computing systems) in “the cloud,” without regard for theunderlying physical systems (or locations of those systems) used toprovide the computing resources.

Typically, cloud computing resources are provided to a user on apay-per-use basis, where users are charged only for the computingresources actually used (e.g. an amount of storage space consumed by auser or a number of virtualized systems instantiated by the user). Auser can access any of the resources that reside in the cloud at anytime, and from anywhere across the Internet. In context of at least oneaspect, a user may access applications (e.g., at least one of the fuelprediction application 112 and the associative memory buildingapplication 114) or related data available in the cloud. For example,the fuel prediction application 112 could execute on a computing systemin the cloud and output the predicted fuel consumption for the plannedvehicle operation. In such a case, the fuel prediction application 112could output the predicted fuel consumption data and store the outputpredicted fuel consumption data at a storage location in the cloud.Doing so allows a user to access this information from any computingsystem attached to a network connected to the cloud (e.g., theInternet).

The use of the term “vehicle” herein is inclusive of any vehicle orvehicle type, such as aircraft, automobiles, boats, ships, trains, andconstruction vehicles.

While the foregoing is directed to certain aspects, other and furtheraspects may be devised without departing from the basic scope thereof,and the scope thereof is determined by the claims that follow.

The invention claimed is:
 1. A computer-implemented method forcalculating a predicted fuel consumption for a planned vehicleoperation, the method comprising: accessing an associative memorydevice, using one or more attributes of an operation plan for theplanned vehicle operation, to determine at least one historical vehicleoperation from a plurality of historical vehicle operations that issimilar to the planned vehicle operation, wherein the associative memorydevice contains data for a plurality of attributes and collected fromthe plurality of historical vehicle operations, and wherein the at leastone historical vehicle operation is determined by applying a respectiveweight defined within the associative memory device to data values foreach of the plurality of attributes; predicting fuel consumption datafor the planned vehicle operation, based on historical fuel consumptiondata corresponding to the at least one historical vehicle operation; andoutputting the predicted fuel consumption data.
 2. Thecomputer-implemented method of claim 1, further comprising: extractingthe one or more attributes from the operation plan; and extracting thehistorical fuel consumption data from the at least one historicalvehicle operation.
 3. The computer-implemented method of claim 2,wherein predicting fuel consumption for the planned vehicle operationfurther comprises: calculating an average of the extracted historicalfuel consumption data; and assigning the calculated average as thepredicted fuel consumption for the planned vehicle operation.
 4. Thecomputer-implemented method of claim 2, wherein determining the at leastone historical vehicle operation from the plurality of historicalvehicle operations that is similar to the planned vehicle operationfurther comprises determining any historical vehicle operations from theplurality of historical vehicle operations with an attribute-matchingscore above a threshold value.
 5. The computer-implemented method ofclaim 4, wherein predicting fuel consumption data for the plannedvehicle operation based on the extracted historical fuel consumptiondata comprises: calculating a weighted average of the extractedhistorical fuel consumption data, wherein weights used in calculatingthe weighted average are based on the attribute-matching scores of thereturned historical vehicle operations; and assigning the calculatedweighted average as the predicted fuel consumption for the plannedvehicle operation.
 6. The computer-implemented method of claim 1,further comprising automatically updating a fuel load of a vehicleoperation plan for the planned vehicle operation based on the outputpredicted fuel consumption data.
 7. The computer-implemented method ofclaim 1, wherein the planned vehicle operation is a flight of acommercial aircraft, and further comprising automatically updating aload sheet for the flight based on the output predicted fuel consumptiondata.
 8. The computer-implemented method of claim 1, wherein an amountof fuel determined by the predicted fuel consumption data is loaded inthe vehicle.
 9. The computer-implemented method of claim 1, wherein anamount of fuel determined by the predicted fuel consumption data plus abuffer of fuel is loaded in the vehicle.
 10. A system, comprising: oneor more computer processors; an associative memory device; and a memorycontaining computer program code that, when executed by operation of theone or more computer processors, performs an operation for calculating apredicted fuel consumption for a planned vehicle operation, theoperation comprising: accessing the associative memory device, using oneor more attributes of an operation plan for the planned vehicleoperation, to determine at least one historical vehicle operation from aplurality of historical vehicle operations that is similar to theplanned vehicle operation, wherein the associative memory devicecontains data for a plurality of attributes and collected from theplurality of historical vehicle operations, and wherein the at least onehistorical vehicle operation is determined by applying a respectiveweight defined within the associative memory device to data values foreach of the plurality of attributes; predicting fuel consumption datafor the planned vehicle operation, based on historical fuel consumptiondata corresponding to the at least one historical vehicle operation; andoutputting the predicted fuel consumption data.
 11. The system of claim10, the operation further comprising: extracting the one or moreattributes from the operation plan; and extracting the historical fuelconsumption data from the at least one historical vehicle operation. 12.The system of claim 11, wherein predicting fuel consumption for theplanned vehicle operation further comprises: calculating an average ofthe extracted historical fuel consumption data; and assigning thecalculated average as the predicted fuel consumption for the plannedvehicle operation.
 13. The system of claim 11, wherein determining theat least one historical vehicle operation from the plurality ofhistorical vehicle operations that is similar to the planned vehicleoperation further comprises determining any historical vehicleoperations from the plurality of historical vehicle operations with anattribute-matching score above a threshold value.
 14. The system ofclaim 13, wherein predicting fuel consumption data for the plannedvehicle operation based on the extracted historical fuel consumptiondata comprises: calculating a weighted average of the extractedhistorical fuel consumption data, wherein weights used in calculatingthe weighted average are based on the attribute-matching scores of thereturned historical vehicle operations; and assigning the calculatedweighted average as the predicted fuel consumption for the plannedvehicle operation.
 15. The system of claim 10, further comprisingautomatically updating a fuel load of a vehicle operation plan for theplanned vehicle operation based on the output predicted fuel consumptiondata.
 16. The system of claim 10, wherein the planned vehicle operationis a flight of a commercial aircraft, and further comprisingautomatically updating a load sheet for the flight based on the outputpredicted fuel consumption data.
 17. The system of claim 10, wherein anamount of fuel determined by the predicted fuel consumption data isloaded in the vehicle.
 18. The system of claim 10, wherein an amount offuel determined by the predicted fuel consumption data plus a buffer offuel is loaded in the vehicle.
 19. A computer-implemented method fordetermining a predicted fuel consumption for a planned vehicleoperation, the method comprising: generating a query configured toreturn predicted fuel consumption data for an operation plan for theplanned vehicle operation; transmitting the generated query to a remotesystem, wherein the remote system is configured to: access anassociative memory device, using one or more attributes of the operationplan for the planned vehicle operation, to determine at least onehistorical vehicle operation from a plurality of historical vehicleoperations that is similar to the planned vehicle operation, wherein theassociative memory device contains data for a plurality of attributesand collected from the plurality of historical vehicle operations, andwherein the at least one historical vehicle operation is determined byapplying a respective weight defined within the associative memorydevice to data values for each of the plurality of attributes; andpredict fuel consumption data for the planned vehicle operation, basedon historical fuel consumption data corresponding to the at least onehistorical vehicle operation; and receiving, from the remote system, thepredicted fuel consumption data.
 20. The computer-implemented method ofclaim 19, wherein an amount of fuel determined based on the predictedfuel consumption data is loaded into a vehicle scheduled to perform theplanned vehicle operation.